The second property of the proposed network is that from the point of view in computer science, the hidden-layer output can be viewed as the output of a hash function. [1]} took this perspective to interpret the Kenyon Cells (KCs) as locality-sensitive tags for odors; the more similar a pair of odors, the more similar their assigned tags. Based on this idea, they developed a brain-inspired class of Locality-Sensitive Hash (LSH) functions [2]} for efficiently searching approximate nearest neighbors of high-dimensional points. Because our formulation is partly derived from those insights, our model also can generate tags that preserve the neighborhood structure of the input points using distance-preserving embedding of points, as a family of LSH. Whereas we have focused on the trade-off between conciseness and expressiveness in the width of the KC hidden layer in artificial neural network applications, [1]} provided computational theory to better understand the performance of architectures matching the scale of the fly brain. Moreover, in their application, a Hebbian-like update rule was used for unsupervised learning of whether an odor had been experienced before (in contrast to the supervised learning we employed). However, their analysis combined with ours implies that KCNet may be employed in unsupervised learning applications, and there will likely be a similar trade-off between the width of the hidden layer and the overall performance of the trained networks.
